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In applications as in wireless communication, computer graphics and digital signal processing, a massive of complex matrix operations is often performed. Reciprocal is computed in large quantities in these matrix operations. To obtain high performance, efficient algorithm and hardware architecture are important in terms of low cost, low computation time and high precision. Second order first sub-function and squaring shrunk method have been proposed to build efficient hardware architecture for reciprocal using field programmable gate array. Second order first sub-function in harmonized parabolic synthesis is presented to improve the approximating precision and decrease the memory usage at the cost of additional multipliers. To further reduce the complexity, squaring shrunk method is proposed to decrease the expensive cost of multipliers. The combination of these techniques yields good performance trade-off. Precision simulation and hardware implementation result has shown that hardware reciprocal of high precision, low memory and low multiplier usage has been obtained compared to traditional first order first sub-function harmonized parabolic synthesis method. [ABSTRACT FROM AUTHOR]

Inverse square root has played an important role in Cholesky decomposition, which devoted to hardware efficient compressed sensing. However, the performance is usually limited by the trade-off between throughput and precision. This paper presents hardware implementation of fixed-point single iterated multiplicative inverse square root. Multiple piecewise linear approximation in softly nonlinear range is used to compute the initial value. Single iterated Newton-Raphson method is employed to obtain high precision. Multiple constants multiplication technique is proposed to achieve high throughput. The combination of these techniques yields high performance in terms of throughput and precision. It obtains more than 70 % of throughput improvement and almost 100 × higher precision over the inverse square root Intellectual Property (IP) from Altera. In addition, Cholesky decomposition has been presented to validate the proposed architecture, which shows that 42 % of throughput improvement is achieved compared with the IP. [ABSTRACT FROM AUTHOR]

With the increasing electricity consumption and the wide application of renewable energy sources, energy auction attracts a lot of attention due to its economic benefits. Many schemes have been proposed to support energy auction in smart grid. However, few of them can achieve range query, ranked search and personalized search. In this paper, we propose an efficient multi-keyword range query (EMRQ) scheme, which can support range query, ranked search and personalized search simultaneously. Based on the homomorphic Paillier cryptosystem, we use two super-increasing sequences to aggregate multidimensional keywords. The first one is used to aggregate one buyer's or seller's multidimensional keywords to an aggregated number. The second one is used to create a summary number by aggregating the aggregated numbers of all sellers. As a result, the comparison between the keywords of all sellers and those of one buyer can be achieved with only one calculation. Security analysis demonstrates that EMRQ can achieve confidentiality of keywords, authentication, data integrity and query privacy. Extensive experiments show that EMRQ is more efficient compared with the scheme in [3] in terms of computation and communication overhead. [ABSTRACT FROM AUTHOR]